Comparing Single‐SNP, Multi‐SNP, and Haplotype‐Based Approaches in Association Studies for Major Traits in Barley
Bibliographic record
Abstract
Core Ideas The multiple single nucleotide polymorphism (multi‐SNP) and haplotype‐based approaches that jointly consider multiple markers unveiled a larger number of associations, some of which were shared with the single‐SNP approach. A larger overlap of quantitative trait loci (QTLs) between the single‐SNP and haplotype‐based approaches was obtained than with the multi‐SNP approach. Despite a limited overlap between the QTLs detected by these approaches, each uncovered QTLs reported previously, suggesting that each approach is capable of uncovering a different subset of QTLs. We demonstrated the efficiency of an integrated genome‐wide association study (GWAS) procedure, combining single‐locus and multilocus approaches to improve the capacity and reliability of association analysis to detect key QTLs. The efficiency of barley breeding programs may be improved by the practical use of QTLs identified in this study. Genome‐wide association studies (GWAS) have been widely used to identify quantitative trait loci (QTLs) underlying complex agronomic traits. The conventional GWAS model is based on a single‐locus model, which may prove inaccurate if a trait is controlled by multiple loci, which is the case for most agronomic traits in barley ( Hordeum vulgare L.). Additionally, an individual single nucleotide polymorphism (SNP) will prove incapable of capturing underlying allelic diversity. A multilocus model could potentially represent a better alternative for QTL identification. This study aimed to explore different GWAS approaches (single‐SNP, multi‐SNP, and haplotype‐based) to establish SNP–trait associations and to potentially describe the complex genetic architecture of seven key traits in spring barley. The multi‐SNP and haplotype‐based approaches unveiled a larger number of significant associations, some of which were shared with the single‐SNP approach. Globally, the multi‐SNP approach explained more of the phenotypic variance (cumulative R 2 ) and provided the best fit with the genetic model [Bayesian information criterion (BIC)]. Compared with the multi‐SNP approach, the single‐SNP and haplotype‐based approaches were relatively similar in terms of cumulative R 2 and BIC, with an improvement with the haplotype‐based approach. Despite limited overlap between detected QTLs, each approach discovered QTLs that had been validated previously, suggesting that each approach can uncover a different subset of QTLs. An integrated GWAS procedure, considering single‐locus and multilocus GWAS approaches jointly, may improve the capacity of association studies to detect key QTLs and to provide a more complete picture of the genetic architecture of complex traits in barley.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".